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#!/usr/bin/env python3 | |
# -*- coding:utf-8 -*- | |
import torch | |
from torch import nn | |
from torch.hub import load_state_dict_from_url | |
__all__ = [ | |
"create_yolox_model", | |
"yolox_nano", | |
"yolox_tiny", | |
"yolox_s", | |
"yolox_m", | |
"yolox_l", | |
"yolox_x", | |
"yolov3", | |
"yolox_custom" | |
] | |
_CKPT_ROOT_URL = "https://github.com/Megvii-BaseDetection/YOLOX/releases/download" | |
_CKPT_FULL_PATH = { | |
"yolox-nano": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_nano.pth", | |
"yolox-tiny": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_tiny.pth", | |
"yolox-s": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_s.pth", | |
"yolox-m": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_m.pth", | |
"yolox-l": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_l.pth", | |
"yolox-x": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_x.pth", | |
"yolov3": f"{_CKPT_ROOT_URL}/0.1.1rc0/yolox_darknet.pth", | |
} | |
def create_yolox_model(name: str, pretrained: bool = True, num_classes: int = 80, device=None, | |
exp_path: str = None, ckpt_path: str = None) -> nn.Module: | |
"""creates and loads a YOLOX model | |
Args: | |
name (str): name of model. for example, "yolox-s", "yolox-tiny" or "yolox_custom" | |
if you want to load your own model. | |
pretrained (bool): load pretrained weights into the model. Default to True. | |
device (str): default device to for model. Default to None. | |
num_classes (int): number of model classes. Default to 80. | |
exp_path (str): path to your own experiment file. Required if name="yolox_custom" | |
ckpt_path (str): path to your own ckpt. Required if name="yolox_custom" and you want to | |
load a pretrained model | |
Returns: | |
YOLOX model (nn.Module) | |
""" | |
from yolox.exp import get_exp, Exp | |
if device is None: | |
device = "cuda:0" if torch.cuda.is_available() else "cpu" | |
device = torch.device(device) | |
assert name in _CKPT_FULL_PATH or name == "yolox_custom", \ | |
f"user should use one of value in {_CKPT_FULL_PATH.keys()} or \"yolox_custom\"" | |
if name in _CKPT_FULL_PATH: | |
exp: Exp = get_exp(exp_name=name) | |
exp.num_classes = num_classes | |
yolox_model = exp.get_model() | |
if pretrained and num_classes == 80: | |
weights_url = _CKPT_FULL_PATH[name] | |
ckpt = load_state_dict_from_url(weights_url, map_location="cpu") | |
if "model" in ckpt: | |
ckpt = ckpt["model"] | |
yolox_model.load_state_dict(ckpt) | |
else: | |
assert exp_path is not None, "for a \"yolox_custom\" model exp_path must be provided" | |
exp: Exp = get_exp(exp_file=exp_path) | |
yolox_model = exp.get_model() | |
if ckpt_path: | |
ckpt = torch.load(ckpt_path, map_location="cpu") | |
if "model" in ckpt: | |
ckpt = ckpt["model"] | |
yolox_model.load_state_dict(ckpt) | |
yolox_model.to(device) | |
return yolox_model | |
def yolox_nano(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-nano", pretrained, num_classes, device) | |
def yolox_tiny(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-tiny", pretrained, num_classes, device) | |
def yolox_s(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-s", pretrained, num_classes, device) | |
def yolox_m(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-m", pretrained, num_classes, device) | |
def yolox_l(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-l", pretrained, num_classes, device) | |
def yolox_x(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox-x", pretrained, num_classes, device) | |
def yolov3(pretrained: bool = True, num_classes: int = 80, device: str = None) -> nn.Module: | |
return create_yolox_model("yolov3", pretrained, num_classes, device) | |
def yolox_custom(ckpt_path: str = None, exp_path: str = None, device: str = None) -> nn.Module: | |
return create_yolox_model("yolox_custom", ckpt_path=ckpt_path, exp_path=exp_path, device=device) | |